Publisher's Synopsis
This book covers a wide typology of advanced econometric models including models of limited dependent variable, discrete choice, count, censored, truncated and sample selection. Also develop models of simultaneous equations, nonlinear models, multivariate time series models, models with panel and unit roots theory data and cointegrated models. In the last chapters the most typical problems of diagnosis are addressed to check in all econometric model, the analysis of variance and covariance, simple and multiple models, the linear model GLM general and mixed models. The development of practical exercises is performed using STATA software. The content of the book is as follows: Limited dependent variable models Discrete choice models Binary discrete choice models Multiple choice models Logit and Probit ordered models Count data models Censored models: the tobit model Sample selection: truncated models Correction the sample selection: heckman two-step estimation or heckit method Limited dependent variable models with STATA Multi-equational linear models. Simultaneous equations Multi-equational linear models. Structural form and simultaneous equations Multi-equational model in reduced form Structural model identification. Simultaneous equations. MCI estimation Simultaneous equations linear model estimation STATA and simultaneous linear equations models Multivariate time series models: VAR, VARX, VARMA and BVAR models. Cointegration Vector autoregressive VAR models VARMA models Cointegration in VAR models. Johansen test STATA and the VAR and VEC models. Causality and cointegration tests. Johansen test Econometrics panel data. Unit roots and cointegration in panel Panel data econometric models Fixed effects panel data models Random -effects panel data models Dynamic panel data models Logit and Probit panel data models Unit roots and cointegration of panel data STATA and panel data models Logit, Probit and Poisson models with panel data Dynamic panel models estimation. Arellano - bond methodology Non-linear models and systems. STATA and non- linear equations models Tests for diagnosis. Autocorrelation, heteroscedasticity, normality, multicollinearity and influence Conditional heteroscedasticity . ARCH and GARCH tests STATA and the multicollinearity, influence, autocorrelation and heteroscedasticity STATA and the multicolinearity, influence, autocorrelation, heteroscedasticity through menus Simple and multiple variance analysis and the simple and multiple covariance models . General lonear models GLM and mixed models STATA and the analysis of the variance-covariance, the GLM model and mixed models
